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Articles by Tag: AI Risk Management

Frontier APIs vs. Open-Weight Models: How Financial Services CIOs Can Improve AI ROI Without Mistaking Token Savings for Value
Strategy Brief

Frontier APIs vs. Open-Weight Models: How Financial Services CIOs Can Improve AI ROI Without Mistaking Token Savings for Value

For a regulated financial institution, replacing a token bill with GPUs does not automatically improve return on investment. It can move costs and accountability into capacity planning, model serving, evaluation, cyber controls, resilience testing, specialist staffing, audit evidence, and incident response.
The New Cost of AI Code Nobody Owns
Tech Sketch

The New Cost of AI Code Nobody Owns

AI-assisted coding is more than a developer-productivity issue, it is a production-accountability issue. This makes the executive decision clear. Permit AI-assisted development broadly, but block material production changes unless a named human can explain, support, secure, and reverse the change.
Use AIBOMs for the Gap Between Approved and Running AI
Featured

Use AIBOMs for the Gap Between Approved and Running AI

AI governance is becoming an evidence problem. CIOs need to prove that production AI systems still match the models, data, prompts, suppliers, and controls originally approved. Continuous AI Bills of Materials turn static inventory into a risk signal, helping leaders detect material change, route accountability, and avoid premature governance tooling.
The Emerging LLM Firewall Market: How to Evaluate Vendors
Tech Sketch

The Emerging LLM Firewall Market: How to Evaluate Vendors

LLM risks are real, but not every deployment needs a firewall. Premature adoption adds cost without reducing exposure. The decision hinges on user trust, data sensitivity, and model autonomy. This guide helps CIOs and CISOs decide when to deploy, how to tier risk, and what to evaluate before committing to a vendor.